Design and Optimization of Liquid Crystal RIS-Based Visible Light Communication Receivers
Bibliographic record
Abstract
In the design of reconfigurable intelligent surfaces (RISs)-aided visible light communication (VLC) systems, most studies have focused on the deployment of mirror arrays and metasurfaces on walls to influence signal propagation and enhance communication performance. This paper provides a new research direction in the design and performance optimization of RIS-aided VLC systems whereby voltage-controlled tunable liquid crystals (LCs) are deployed as part of the VLC receiver. The purpose of the LC RIS is to provide incident light steering and intensity amplification in order to improve the received signal strength and the corresponding achievable data rate. More specifically, an LC RIS-based VLC receiver design is proposed and its operating principles and the channel model for a VLC system with such a receiver are provided. Since the refractive index of the LC RIS plays a critical role in the wave-guiding and light amplification capabilities of this novel receiver, a rate maximization problem is considered to achieve the optimal refractive index and the required voltage to obtain the best light amplification and data rate performances. This communication design problem is a non-convex optimization problem for which a metaheuristic approach is developed based on the sine-cosine algorithm. Simulation results are used to confirm the considerable data rate improvement by the proposed LC RIS-based VLC receiver and optimization algorithm when compared to a VLC receiver without the LC RIS and a baseline scheme, respectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".